Results for “research”

Pre-meeting research and talking points generator. Researches the person and company, pulls CRM history, and generates a structured briefing with agenda, talking points, objection handlers, and recommended asks.

Verify research idea novelty against recent literature. Use when user says "查新", "novelty check", "有没有人做过", "check novelty", or wants to verify a research idea is novel before implementing.

Tavily web search, content extraction, and research tools.

端到端研究编排器：idea 发现 → 实验设计 → 执行 → 判决 → 论文撰写，带人工门控和状态恢复

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.

Generates copyable scientific figure prompts from research documents, papers, and reference images for AI illustration models.

Manages a persistent hypothesis tree in markdown, tracking falsifiable claims with linked evidence, timestamps, and next experiments for multi-step research investigations.

Research the open web for enhancement opportunities (Layer 3 — Opus subagent with WebSearch + WebFetch). Topic-driven. Returns structured findings with hype tags + source URLs. Does NOT auto-implement.

Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.

Performs web search, content extraction, site crawling, URL discovery, and deep research via the Tavily CLI, returning JSON for LLM consumption.

Adds Claude's built-in WebSearch as a third research source for the last30days command, enabling zero-config operation while preserving Reddit/X primacy through engagement-adjusted scoring.

进行市场研究、竞争分析、投资者尽职调查和行业情报，附带来源归属和决策导向的摘要。适用于用户需要市场规模、竞争对手比较、基金研究、技术扫描或为商业决策提供信息的研究时。

Stateful validator-gated research loop with native-hook persistence

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a.

Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process: (1) create section outlines with key points using research-lookup, (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.

Conducts multi-turn iterative deep research on specific topics within a codebase, tracing actual code paths and grounding every claim in evidence.

Build RAG pipelines that combine web search and LLMs for research, fact-checking, and grounded responses using the inference.sh CLI.

Guides a structured interview and repository research to clarify feature requirements, classify scope, and produce context artifacts for approval before design.

Identifies, qualifies, and tiers investors for a funding round based on stage, sector, geography, check size, and portfolio fit, delivering a prioritized target list.

Produces structured US equity research and trading support: company snapshots, earnings analysis, valuation scenarios, catalysts, risks, and action plans with falsifiable claims.

Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.

Research purchases, compare prices, detect scams, and negotiate better deals.

Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for mechanistic interpretability research.

Coordinates multiple research agents to gather information on a topic across GitHub, HuggingFace, papers, competitors, and best practices, then compiles findings into a structured report.

Split an idea or active research path into two or more new research product-path stubs after alignment approval; preserves the source path by default and archives it only when --archive is passed

Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT for library/framework docs (use Context7) or workspace questions.

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.

Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".

Create data-backed customer personas with market research, demographics, psychographics, jobs-to-be-done, journey mapping, and avatar generation.

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

Support accounting understanding from basic bookkeeping to professional practice and research.

统一入口和智能路由中心，自动识别用户意图并路由到相应技能。基于 Cue 产品设计，提供专业化的深度研究体验。

Full autonomous research workflow — brainstorm, plan, implement, review, and document

Full autonomous research workflow using swarm mode for parallel execution

Standardized Python & Stata coding practices for empirical research projects

Project directory organization and script naming conventions for research